November 28, 2025
Pipecat Thanksgiving day release. 🦃
Some highlights: Deepgram AWS SageMaker realtime speech-to-text support, improved text aggregation, simplified and more powerful error handling, new MiniMax Speech 2.6 HD and Turbo models.
SageMaker is AWS's AI platform for deploying and using machine learning models at scale. AWS has brand new support for streaming data in and out of models hosted on SageMaker, which is great for voice AI use cases. This Pipecat release includes a generic base class for SageMaker "bidirectional streaming," plus a new `DeepgramSageMakerSTTService` class.
Text aggregation and error handling are important fundamental jobs that a realtime agent framework needs to do well for the widest possible range of models, APIs, and use cases.
Different APIs chunk streaming text differently. For different use cases, you might want different aggregation strategies. (For example, feed one sentence of LLM output at a time to your voice generation service.) And managing multi-turn context as accurately as possible requires different strategies depending on what the APIs you are using can do. (For example, whether your TTS model can give you word-level timestamps or not).
Good error handling requires both managing the very different approaches to error handling that different services have, and giving developers good application-level ways to catch, respond to, and log errors.
The more services Pipecat supports, and the more different kinds of things people use Pipecat for, the more work these abstraction layers need to do! This Pipecat release includes several new text aggregation and error handling frame types and methods. The goal of these improvements is to make common use cases work better with less application-level code required, while also making it easier to build robust error handling for complex applications.
Finally, the MiniMax Speech models are getting great reviews. Thank you to the MiniMax team for the implementation!
Full changelog[1]